A cerebral aneurysm is a weakened and diseased portion of an artery in the brain. When a cerebral aneurysm ruptures, the brain is bathed in blood, and a specific type of bleeding known as a subarachnoid hemorrhage (SAH) occurs. Approximately fifty percent of patients die immediately after an aneurysm ruptures, and no test exists currently to screen people for the presence of a cerebral aneurysm. The diagnosis of a SAH is made only after an aneurysm ruptures, and the literature indicates that 23-51% of patients with a SAH are originally misdiagnosed with the average delay in diagnosis being 6 days. At present, clinicians do not possess the diagnostic modalities sufficient for assessing patients with a suspected ruptured cerebral aneurysm and a normal computerized tomography (CT) scan.
In patients with a suspected SAH and a negative CT scan, the performance of a lumbar puncture (LP) is strongly recommended. Typically, a needle is used to sample the fluid surrounding the spinal cord in LP. Collected from the patient's lumbar spine, the cerebrospinal fluid communicates with the fluid around the brain and is tainted with blood after an aneurysm ruptures. In other words, the presence of blood in the CSF raises the possibility of a SAH. The difficulty with the performance of a spinal tap is that nearly 1 in 5 of these procedures results in a “traumatic tap”. This term implies the needle used for collection of the CSF penetrated a blood vessel in the patient's back prior to entering the space where the CSF is found. In these circumstances, part of the sample is the patient's CSF and a portion is from a blood vessel in the muscle, bone or a ligament of the spine. At present, no test can reliably identify the source of blood in CSF and differentiate a SAH from a traumatic spinal tap. A correct diagnosis is critical to directing patients toward repair of their cerebral aneurysm before another rupture occurs.
About 2 million people in the United States are thought to harbor a cerebral aneurysm, while an SAH affects 30,000 people per year. To detect a small or sentinel SAH, CSF analysis should be rapid and sensitive. Likewise, the specificity of CSF analysis is crucial, and the misinterpretation of a traumatic spinal tap as a SAH may lead to the treatment of an un-ruptured aneurysm. If the CSF contains zero or very few red blood cells, a diagnosis of SAH is extremely unlikely. The difficulty arises when large numbers of (red blood cells) RBC's are found. At present, no method of CSF analysis has the ability to fully direct a patient's care.
A key concept to the differentiation of a SAH from a traumatic tap is the identification of a marker for SAH. The measurement of CSF bilirubin, if collected 12 hours after the onset of symptoms, can distinguish between a SAH and a traumatic spinal tap. Furthermore, the production of bilirubin occurs over a predictable time-course and can be detected following a low volume SAH. Effectively, the presence of elevated CSF bilirubin excludes the diagnosis of a traumatic tap. Hence, methods are needed for rapidly assessing a condition of elevated CSF bilirubin in an individual suspected of having suffered from an SAH.
The determination of blood in the CSF is frequently done simply with a visual inspection for xanthochromia, a discoloration of the CSF indicating the presence of blood. This subjective and low sensitivity technique cannot determine the amount of blood in the CSF or the degradative processes that have acted on the blood. Nor can this visual inspection estimate the amount of time the blood has been in the CSF. The results of visual interpretation of spectra are qualitative in nature and depend strongly on the experience of the technicians; results, therefore, are prone to large inter- and intra-individual variation. Thus, there exists a substantial need for improved methods for analyzing blood in the CSF to assess a condition, such as a hemorrhage and methods are needed which objectively quantify spectral output and do not depend on the skills of the technician.
Similar needs existed and were developed in the field of diabetic study to quantify glucose in blood. In measurements of blood glucose, the doctors withdraw some quantity of blood and analyze it. To extract the glucose information in the presence of other constituents, spectral analysis is performed where different physiological components produce spectral signals. The main idea is to separate the glucose signals from the other dominating signals in the spectra. There have been a number of ways to find a solution for this problem using mathematical algorithms. These include, for example, the methods of principal components regression (PCR), partial least squares (PLS), and artificial neural networks (ANN).
Performance reliability and validity of the signal processing is very dependent on the quality of the data. The PLS and the ANN algorithmic models are not always dependable because they are sensitive to the changes in time and the resultant variance in data that varies with the concentration of the solution. Moreover, given their complexity they are more appropriate to implement when multiple variables need to be predicted. Other work has concentrated on using pattern recognition algorithms to extract the respective signals. It has been reported that derivative analysis of the absorption or transmittance spectra can be a useful tool in drastically improving the selectivity of a bilirubin in a mixed component sample. Others have tried to use an extension to the above model by characterizing the absorbance/transmittance curves by Gaussian peaks and then applying the first derivative algorithm. The problem with this work is that the results have been obtained in very diluted samples that are not physiologically achievable and hence have no application to assessment of a condition in an individual by subjecting fluid samples drawn from the individual to those analyses. Recent research has concentrated on the first and the second order derivative applications on the spectral signals of bilirubin and other components to analyze patterns that reflect changes that can be used in analysis. Also, other mathematical algorithms like logarithmic ratios of signals are compared to extract the wanted component. None of the above work has been applied to describe aneurysm models or SAH models.